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Non-intrusive load monitoring method and system based on deep learning

A non-invasive, load monitoring technology, applied in neural learning methods, image data processing, image enhancement, etc., can solve problems such as space-time laws that cannot simulate electrical characteristics of electrical appliances, avoid gradient disappearance and gradient explosion, and improve accuracy. Effect

Pending Publication Date: 2021-12-17
HEFEI UNIV OF TECH
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  • Claims
  • Application Information

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Problems solved by technology

[0005] Aiming at the deficiencies of the prior art, the present invention provides a non-intrusive load monitoring method and system based on deep learning, which solves the technical problem that the existing non-intrusive load monitoring cannot simulate the spatio-temporal laws of electrical characteristics of electrical appliances

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  • Non-intrusive load monitoring method and system based on deep learning
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Embodiment Construction

[0053]In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. example. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0054] The embodiment of the present application provides a non-intrusive load monitoring method and system based on deep learning, which solves the technical problem that the existing non-intrusive load monitoring cannot simulate the spatio-temporal law of the electrical characteristics of electrical appliances, and realizes that the data can be taken into account when monitoring. The time dependence in the system improves the accu...

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Abstract

The invention provides a non-intrusive load monitoring method and system based on deep learning, and relates to the technical field of power load monitoring. According to the method, one-dimensional power data is converted into to-be-monitored two-dimensional image data, time domain information is brought into original data, so more effective information is contained in the data; meanwhile, a non-intrusive load monitoring model based on deep learning is used, and the model can consider time dependence in the data, so the precision of the prediction model is improved, and gradient disappearance and gradient explosion in the CNN network can be avoided.

Description

technical field [0001] The invention relates to the technical field of electric load monitoring, in particular to a non-invasive load monitoring method and system based on deep learning. Background technique [0002] With the rapid growth of the population and the rapid development of the economy, the number and types of users' electrical equipment are increasing rapidly, and the analysis algorithms for different users' energy consumption habits have received more and more attention. Non-intrusive load monitoring does not need to install additional sub-meters on each electrical equipment, only through the data in a single general table, analyze and dig out the operating status of each electrical appliance counted in the general table, including start-stop time, service cycle, etc. . Non-intrusive load monitoring can provide users with detailed energy bills and personalized energy-saving suggestions, monitor faulty equipment, help customer segmentation, and enhance microgrid...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/04G06N3/08G06T3/40
CPCG06T7/0004G06T3/4046G06N3/08G06T2207/10004G06N3/045
Inventor 周开乐殷辉丁涛李兰兰周昆树胡定定
Owner HEFEI UNIV OF TECH